Job analysis is working
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@ -49,7 +49,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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const { apiClient } = useAuth();
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const theme = useTheme();
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const [jobRequirements, setJobRequirements] = useState<JobRequirements | null>(null);
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const [requirements, setRequirements] = useState<string[]>([]);
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const [requirements, setRequirements] = useState<{ requirement: string, domain: string }[]>([]);
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const [skillMatches, setSkillMatches] = useState<SkillMatch[]>([]);
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const [creatingSession, setCreatingSession] = useState<boolean>(false);
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const [loadingRequirements, setLoadingRequirements] = useState<boolean>(false);
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@ -102,23 +102,24 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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console.log(`onMessage: ${msg.type}`, msg);
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if (msg.type === "response") {
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const incoming: any = toCamelCase<JobRequirements>(JSON.parse(msg.content || ''));
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const requirements: string[] = ['technicalSkills', 'experienceRequirements'].flatMap((type) => {
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const requirements: { requirement: string, domain: string }[] = ['technicalSkills', 'experienceRequirements'].flatMap((domain) => {
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return ['required', 'preferred'].flatMap((level) => {
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return incoming[type][level].map((s: string) => s);
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return incoming[domain][level].map((s: string) => { return { requirement: s, domain: domain }; });
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})
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});
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['softSkills', 'experience', 'education', 'certifications', 'preferredAttributes'].forEach(l => {
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if (incoming[l]) {
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incoming[l].forEach((s: string) => requirements.push(s));
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['softSkills', 'experience', 'education', 'certifications', 'preferredAttributes'].forEach(domain => {
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if (incoming[domain]) {
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incoming[domain].forEach((s: string) => requirements.push({ requirement: s, domain: domain }));
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}
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});
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// Initialize skill matches with pending status
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const initialSkillMatches = requirements.map(req => ({
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requirement: req,
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requirement: req.requirement,
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domain: req.domain,
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status: 'pending' as const,
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matchScore: 0,
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assessment: '',
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description: '',
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citations: []
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}));
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@ -167,8 +168,27 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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// Process requirements one by one
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for (let i = 0; i < requirements.length; i++) {
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try {
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const match: SkillMatch = await apiClient.candidateMatchForRequirement(candidate.id || '', requirements[i]);
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console.log(match);
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const result: any = await apiClient.candidateMatchForRequirement(candidate.id || '', requirements[i].requirement);
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const skillMatch = result.skillMatch;
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let matchScore: number = 0;
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switch (skillMatch.evidenceStrength) {
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case "STRONG": matchScore = 100; break;
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case "MODERATE": matchScore = 75; break;
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case "WEAK": matchScore = 50; break;
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case "NONE": matchScore = 0; break;
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}
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if (skillMatch.evidenceStrength == "NONE" && skillMatch.citations.length > 3) {
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matchScore = Math.min(skillMatch.citations.length * 8, 40);
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}
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const match: SkillMatch = {
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status: "complete",
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matchScore,
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domain: requirements[i].domain,
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requirement: skillMatch.skill,
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assessment: skillMatch.assessment,
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citations: skillMatch.evidenceDetails.map((evidence: any) => { return { source: evidence.source, text: evidence.quote, context: evidence.context } }),
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description: skillMatch.description
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};
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setSkillMatches(prev => {
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const updated = [...prev];
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updated[i] = match;
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@ -341,9 +361,14 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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}}>
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<Box sx={{ display: 'flex', alignItems: 'center' }}>
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{getStatusIcon(match.status, match.matchScore)}
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<Typography sx={{ ml: 1, fontWeight: 'medium' }}>
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<Box sx={{ display: "flex", flexDirection: "column", gap: 0, p: 0, m: 0 }}>
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<Typography sx={{ ml: 1, mb: 0, fontWeight: 'medium', marginBottom: "0px !important" }}>
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{match.requirement}
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</Typography>
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<Typography variant="caption" sx={{ ml: 1, fontWeight: 'light' }}>
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{match.domain}
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</Typography>
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</Box>
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</Box>
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{match.status === 'complete' ? (
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@ -356,6 +381,12 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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minWidth: 90
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}}
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/>
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) : match.status === 'waiting' ? (
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<Chip
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label="Waiting..."
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size="small"
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sx={{ bgcolor: "rgb(189, 173, 85)", color: 'white', minWidth: 90 }}
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/>
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) : match.status === 'pending' ? (
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<Chip
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label="Analyzing..."
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@ -387,7 +418,7 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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) : (
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<Box>
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<Typography variant="h6" gutterBottom>
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Assessment:
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Assessment
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</Typography>
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<Typography paragraph sx={{ mb: 3 }}>
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@ -395,9 +426,8 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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</Typography>
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<Typography variant="h6" gutterBottom>
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Supporting Evidence:
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</Typography>
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Supporting Evidence
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</Typography>
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{match.citations && match.citations.length > 0 ? (
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match.citations.map((citation, citIndex) => (
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<Card
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@ -413,17 +443,20 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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<Typography variant="body1" component="div" sx={{ mb: 1, fontStyle: 'italic' }}>
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"{citation.text}"
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</Typography>
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<Box sx={{ display: 'flex', justifyContent: 'space-between', alignItems: 'center' }}>
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<Box sx={{ display: 'flex', justifyContent: 'space-between', alignItems: 'flex-start', flexDirection: "column" }}>
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<Typography variant="body2" color="text.secondary">
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Relevance: {citation.context}
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</Typography>
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<Typography variant="caption" color="text.secondary">
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Source: {citation.source}
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</Typography>
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<Chip
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{/* <Chip
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size="small"
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label={`Relevance: ${citation.relevance}%`}
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sx={{
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bgcolor: theme.palette.grey[200],
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}}
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/>
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/> */}
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</Box>
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</CardContent>
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</Card>
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@ -433,6 +466,13 @@ const JobMatchAnalysis: React.FC<JobAnalysisProps> = (props: JobAnalysisProps) =
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No specific evidence found in candidate's profile.
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</Typography>
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)}
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<Typography variant="h6" gutterBottom>
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Skill description
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</Typography>
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<Typography paragraph>
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{match.description}
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</Typography>
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</Box>
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)}
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</AccordionDetails>
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@ -1,6 +1,6 @@
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// Generated TypeScript types from Pydantic models
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// Source: src/backend/models.py
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// Generated on: 2025-06-04T03:59:11.250216
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// Generated on: 2025-06-04T05:16:43.020718
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// DO NOT EDIT MANUALLY - This file is auto-generated
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// ============================
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@ -49,7 +49,7 @@ export type SearchType = "similarity" | "mmr" | "hybrid" | "keyword";
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export type SkillLevel = "beginner" | "intermediate" | "advanced" | "expert";
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export type SkillStatus = "pending" | "complete" | "error";
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export type SkillStatus = "pending" | "complete" | "waiting" | "error";
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export type SocialPlatform = "linkedin" | "twitter" | "github" | "dribbble" | "behance" | "website" | "other";
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@ -391,6 +391,7 @@ export interface ChromaDBGetResponse {
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export interface Citation {
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text: string;
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source: string;
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context: string;
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relevance: number;
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}
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@ -835,10 +836,12 @@ export interface SkillAssessment {
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export interface SkillMatch {
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requirement: string;
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status: "pending" | "complete" | "error";
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domain: string;
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status: "pending" | "complete" | "waiting" | "error";
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matchScore: number;
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assessment: string;
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citations?: Array<Citation>;
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description: string;
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}
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export interface SocialLink {
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@ -73,7 +73,8 @@ a SPECIFIC skill based solely on their resume and supporting evidence.
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"skill": "{skill}",
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"evidence_found": true/false,
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"evidence_strength": "STRONG/MODERATE/WEAK/NONE",
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"description": "short (two to three sentence) description of what {skill} means with a concise example of what you're looking for",
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"assessment": "short (one to two sentence) assessment of the candidate's proficiency with {skill}",
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"description": "short (two to three sentence) description of what the {skill} is, independent of whether the candidate has that skill or not",
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"evidence_details": [
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{{
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"source": "resume section/position/project",
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@ -182,20 +183,9 @@ JSON RESPONSE:"""
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return
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json_str = self.extract_json_from_text(skill_assessment.content)
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skill_match = json_str#: SkillMatch | None = None
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skill_assessment_data = ""
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try:
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skill_assessment_data = json.loads(json_str)
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match_level = (
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skill_assessment_data
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.get("skill_assessment", {})
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.get("evidence_strength", "UNKNOWN")
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)
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skill_description = (
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skill_assessment_data
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.get("skill_assessment", {})
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.get("description", "")
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)
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skill_assessment_data = json.loads(json_str).get("skill_assessment", {})
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except json.JSONDecodeError as e:
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status_message.status = ChatStatusType.ERROR
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status_message.content = f"Failed to parse Skill assessment JSON: {str(e)}\n\n{skill_assessment_data}"
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@ -215,15 +205,9 @@ JSON RESPONSE:"""
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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if skill_match is None:
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status_message.status = ChatStatusType.ERROR
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status_message.content = "Skill assessment analysis failed to produce valid data."
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logger.error(f"⚠️ {status_message.content}")
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yield status_message
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return
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status_message.status = ChatStatusType.DONE
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status_message.type = ChatMessageType.RESPONSE
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status_message.content = skill_match
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status_message.content = json.dumps(skill_assessment_data)
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yield status_message
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logger.info(f"✅ Skill assessment completed successfully.")
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@ -3395,8 +3395,8 @@ async def get_candidate_skill_match(
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status_code=500,
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content=create_error_response("NO_MATCH", "No skill match found for the given requirement")
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)
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skill_match = skill_match.content.strip()
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logger.info(f"✅ Skill match found for candidate {candidate.id}: {skill_match}")
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skill_match = json.loads(skill_match.content)
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logger.info(f"✅ Skill match found for candidate {candidate.id}: {skill_match['evidence_strength']}")
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return create_success_response({
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"candidateId": candidate.id,
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@ -87,19 +87,23 @@ class Requirements(BaseModel):
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class Citation(BaseModel):
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text: str
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source: str
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context: str
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relevance: int # 0-100 scale
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class SkillStatus(str, Enum):
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PENDING = "pending"
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COMPLETE = "complete"
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WAITING = "waiting"
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ERROR = "error"
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class SkillMatch(BaseModel):
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requirement: str
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domain: str
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status: SkillStatus
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match_score: int = Field(..., alias='matchScore')
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assessment: str
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citations: List[Citation] = Field(default_factory=list)
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description: str
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model_config = {
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"populate_by_name": True # Allow both field names and aliases
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}
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